Browsing by Author "Jenkins, Karl"

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    Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach
    (Springer, 2024-12-31) Thomas, Julien; Kuang, Boyu; Wang, Yizhong; Barnes, Stuart; Jenkins, Karl
    The implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception.
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    Code and data supporting 'A Comprehensive Analysis of Machine Learning and Deep Learning Models for Identifying Pilots' Mental States from Imbalanced Physiological Data'
    (Cranfield University, 2023-09-18 16:40) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl; Yadav, Satendra
    Data: This folder contains: - A dataset called combined_df4, which contains the power spectral density features after employing SMOTE. - A dataset called combined_df5, which contains the power spectral density features after employing SMOTE and cosine similarity. Source code: This folder contains: - A jupyter notebook called AdaBoost.ipynb which was used to generate the results for the AdaBoost algorithm. - A jupyter notebook called CNN.ipynb which was used to generate the results for the CNN algorithm. - A jupyter notebook called CNN+LSTM.ipynb which was used to generate the results for the CNN+LSTMalgorithm. - A jupyter notebook called LSTM.ipynb which was used to generate the results for the LSTMalgorithm. - A jupyter notebook called FNN.ipynb which was used to generate the results for the FNN algorithm. - A jupyter notebook called Random_Forest.ipynb which was used to generate the results for the Random Forest algorithm. - A jupyter notebook called XGBoost.ipynb which was used to generate the results for the XGBoost algorithm.
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    Code and Data: Multimodal Approach for Pilot Mental State Detection Based on EEG
    (Cranfield University, 2023-08-23 15:04) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl
    Data: This folder contains: A dataset called Crews_equalized_dataset_epo.fif which was used to obtain the results presented in the journal paper. It is the preprocessed EEG dataset used to predict four mental states, Channelised Attention, Diverted Attention, Startle/Surprise, and Baseline. A dataset called Example_raw.fif which was used to obtain Figure 6 of the journal paper. Source code: This folder contains a jupyter notebook called python_code.ipynb which implements the proposed EEG preprocessing pipeline and all the algorithms presented and validated in the journal paper. Output: This folder contains: A figure called Confusion Matrices.jpg which shows results from the Random Forest classifier in (A), Extremely Randomized Trees in (B), Gradient Tree Boosting in (C), AdaBoost in (D), and Voting in (E). Figures called Figure 6A.jpg and Figure 6B.jpg which show the EEG signals before applying the preprocessing pipeline, and after applying the preprocessing pipeline, respectively. A text file called ML models evaluation.txt which contains the results produced by all algorithms presented and validated in the journal paper. A figure called The preprocessed EEG signals.jpg which shows the EEG signals, upon completion of our preprocessing pipeline, fed into the machine learning models for training and testing purposes.
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    Data supporting "Analysing the Sentiment of Air-Traveller: A Comparative Analysis"
    (Cranfield University, 2022-08-31 12:55) Salih A Homaid, Mohammed; Bala Bisandu, Desmond; Moulitsas, Irene; Jenkins, Karl
    Airport service qualityis considered to be an indicator of passenger satisfaction. However, assessingthis by conventional methods requires continuous observation and monitoring.Therefore, during the past few years, the use of machine learning techniquesfor this purpose has attracted considerable attention for analysing thesentiment of the air traveller. A sentiment analysis system for textual dataanalytics leverages the natural language processing and machine learningtechniques in order to determine whether a piece of writing is positive, negativeor neutral. Numerous methods exist for estimating sentiments which includelexical-based methodologies and directed artificial intelligence strategies.Despite the wide use and ubiquity of certain strategies, it remains unclearwhich is the best strategy for recognising the intensity of the sentiments of amessage. It is necessary to compare these techniques in order to understandtheir advantages, disadvantages and limitations. In this paper, we compared theValence Aware Dictionary and sentiment Reasoner, a sentiment analysis techniquespecifically attuned and well known for performing good on social media data,with the conventional machine learning techniques of handling the textual databy converting it into numerical form. We used the review data obtained from theSKYTRAX website for each airport. The machine learning algorithms evaluated inthis paper are VADER sentiment and logistic regression. The termfrequency-inverse document frequency is used in order to convert the textualreview datainto the resulting numerical columns. This was formulated as a classificationproblem, whereby the prediction of the algorithm was compared with the actualrecommendation of the passenger in the dataset. The results were analysedaccording to the accuracy, precision, recall and F1-score. From the analysis ofthe results, we observed that logistic regression outperformed the VADERsentiment analysis.
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    Supporting code and data for 'Miscellaneous EEG Preprocessing and Machine Learning for Pilots' Mental States Classification: Implications'
    (Cranfield University, 2023-09-18 16:10) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl
    Data: This folder contains: - A dataset called Pilot_5_CA_raw.fif, which contains the EEG data of a pilot when he was experiencing the channelised attention state in a non-flight environment. - A dataset called Pilot_5_DA_raw.fif, which contains the EEG data of a pilot when he was experiencing the diverted attention state in a non-flight environment. - A dataset called Pilot_5_SS_raw.fif, which contains the EEG data of a pilot when he was experiencing the startle/surprise state in a non-flight environment. - A dataset called Pilot_5_LOFT_raw.fif, which contains the EEG data of a pilot when he was experiencing the channelised attention, diverted attention, and startle/surprise state in a flight simulator environment. Source code: This folder contains: - A jupyter notebook called ICAAI_Conference.ipynb which was used to generate the results of the study. - A python file called cf_matrix which was used to plot the confusion matrix
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    Virtual reality's effects on air crash accident investigation learning interaction
    (Institute of Advanced Engineering and Science, 2023-07-01) Alburaidi, Faisal Othman H.; Jenkins, Karl; Teschner, Tom-Robin
    The objective of this study is to gain a deeper comprehension of the factors that play a role in the evolution of virtual reality (VR) for application in the investigation of aviation disasters. This study was motivated by the concept of utilising VR to create an illusion of the procedures involved in an aviation accident. A conceptual model has been presented, to create the scene that will be simple to understand the procedure of accident following an air crash. The idea is to compile a series of steps that an investigation team will go through and then present them in VR. This stage entails obtaining complete views of the object before it crashes and at ground level. This study contributes to the process of creating the groundwork for adopting VR in air crash investigations to provide instructional experiences. The idea that was presented in this research focuses on feature of the surrounding area of the crash or accident, wreckage distribution, wreckage above the ground, wreckage in motion, wreckage at the ground, spatial view effect, and full view projection as the primary VR features that are required for teaching and learning about air crash accident investigations using VR.